Search Results (3,056)

This study directly compares a photovoltaic (PV)-powered vapor-compression chiller with a solar-thermal-driven absorption chiller for localized cooling of the tomato crop zone in a 1536 m² smart greenhouse under a hot-arid climate. The principal contribution is a controlled system-level comparison of two solar-cooling pathways under the same greenhouse load, solar-aperture area, terminal equipment, rated cooling capacity, and crop-zone temperature-control constraints. The previously validated greenhouse model was transitioned from EnergyPlus 8.9 to Version 23.1, after which the two alternative plants were connected to the same base model. Base-case annual simulations produced nearly identical chiller cooling energy (1669.3 and 1668.9 MWh) and was only 4 and 5 h above 28 °C. The PV-powered system required 101.6 MWh of net grid electricity, whereas the absorption system used 202.3 MWh of electricity and 253.2 MWh of natural gas and achieved an 84.23% solar fraction. Static operational primary energy was 331.2 and 912.7 MWhPE, respectively; HSDH28 was 0.50 and 0.81 °C·h; and peak grid import was 113.46 and 63.61 kW. The absorption case additionally required 10,103.6 m³/yr of cooling-tower makeup water. Storage/EMS sensitivity changed the absorption solar fraction from 58.17% to 88.30% and natural-gas use from 187.7 to 674.0 MWh/yr without materially changing cooling service. Matched 50–100 W/m² daytime latent-load sensitivity increased annual cooling by 14.7–28.8%. At the 100 W/m² bound, HSDH28 increased to 49.32 °C·h for PV and 8.06 °C·h for absorption, while the principal energy–infrastructure trade-off remained: static primary energy was 712.4 versus 1354.2 MWhPE and peak grid import was 137.46 versus 63.96 kW. A bounded hourly primary-energy-factor stress test did not reverse the technology ranking, and balanced TOPSIS scores were 0.766 for PV and 0.234 for absorption. The results show that PV vapor compression minimizes operational primary energy and cooling-water use, whereas solar-thermal absorption reduces electrical peak demand and shows greater thermal-control resilience at the highest tested latent-load bound. Full article

Higher education institutions are increasingly expected to reduce greenhouse gas emissions while maintaining reliable educational, laboratory, and administrative operations. This challenge is particularly relevant in transition economies, where university campuses often depend on fossil-fuel-based electricity systems, natural-gas heating, and aging infrastructure. This study presents a campus-scale decarbonization assessment for the Tashkent Institute of Chemical Technology in Uzbekistan. The quantified inventory covered Scope 1 emissions from natural-gas combustion and Scope 2 emissions from purchased electricity. Paper use, digital services, behavioural measures, and campus greening were assessed as supplementary institutional indicators and were excluded from the quantified total because consistent pre- and post-intervention activity data were unavailable. The assessment combined institutional utility records for 2023–2025 with information on renewable-energy deployment, heating modernization, digital transformation, sustainability awareness, and campus greening. A 300 kW solar photovoltaic system comprising 666 modules was commissioned in May 2023, with a documented annualized generation potential of approximately 520,000 kWh. Purchased grid electricity amounted to 711,402, 745,947, and 749,060 kWh in 2023, 2024, and 2025, respectively, while annual natural-gas consumption was 144,775, 161,200, and 142,031 m³. Using a conservative standard-based net calorific value of 31.8 MJ/m³ together with IPCC stationary-combustion factors, annual Scope 1 and Scope 2 emissions were estimated at 637.53, 685.29, and 652.65 tCO₂-eq, respectively. The 2025 total was 4.76% below the 2024 value but 2.37% above the 2023 value. The annualized PV technical potential corresponds to a theoretical maximum Scope 2 displacement of 276.64 tCO₂-eq/year under 100% self-consumption. This value does not represent measured generation or a realized emission reduction and was not included in the quantified inventory. Digitalization, behavioural engagement, and greening were evaluated as complementary measures rather than assigned separate emission-reduction credits. The study provides a transparent and regionally relevant framework for universities in transition economies seeking to strengthen campus carbon management under incomplete data conditions. Full article

Two-dimensional transition-metal carbides and nitrides (MXenes) are increasingly adopted in advanced electronic devices, where their metallic conductivity, optical tunability, and chemically addressable surfaces support next-generation multifunctional optoelectronics. Their practical performance, however, depends not only on their intrinsic properties but also on the heterogeneous interfaces where charges, photons, and ions interact. Unlike earlier reviews organized around synthesis routes or separate device categories, this review takes interfacial chemistry as a single organizing principle and follows it from surface terminations through to integrated systems. The structural and surface-chemical characteristics of MXenes are described first, showing how dynamic terminations and interfacial dipoles regulate work functions and energy-level alignment. We then discuss molecular functionalization, defect passivation, and heterojunction formation as strategies for reducing Schottky barriers and improving charge-transfer kinetics. Optoelectronic platforms built on these engineered interfaces, including high-efficiency photovoltaics, broadband photodetectors, and stretchable wearable systems, are subsequently detailed, together with emerging architectures that merge self-powered sensing with neuromorphic visual functions, a scope seldom treated alongside conventional devices in previous surveys. By connecting surface chemistry with device integration, this review outlines a materials-to-systems pathway toward more reliable and scalable MXene-based optoelectronic technologies. Full article

Data centers can provide demand-side flexibility by coordinating computing workloads, cooling systems, and on-site energy resources. However, facility demand varies with information technology (IT) load and cooling operation, making a fixed power usage effectiveness (PUE) or a forecast independent of planned controls inconsistent with dispatch. This paper proposes a control-aware, multi-horizon PUE forecasting framework for coordinated data center demand-side management (DSM) and microgrid dispatch. The key idea of this control-aware approach is to forecast PUE using planned workload and cooling schedules as inputs. A power-consistent Temporal Fusion Transformer (PC-TFT) predicts quantiles of non-IT overhead power and rack inlet temperature from telemetry, weather forecasts, admitted requests, and candidate workload and cooling schedules. Facility power and PUE are derived from the algebraic power balance, ensuring consistency among IT, overhead, and facility power and PUE values no lower than 1. Empirical split conformal calibration and temporally dependent scenarios characterize forecast uncertainty. A trajectory-conditioned piecewise-affine control response map with a recursive thermal state links the forecasts to a risk-informed model predictive controller that coordinates workloads, cooling, photovoltaic generation, battery storage, and grid exchange. The proposed framework is validated through EnergyPlus simulations of a Shenzhen data center, coupled with workload and microgrid simulations. Forecasting performance is compared with persistence and matched-input neural baselines, while dispatch is benchmarked against deterministic and oracle controllers. The results demonstrate improved multi-horizon PUE forecasting accuracy and empirical interval calibration, lower operating cost and peak grid demand, higher renewable energy utilization, and fewer service quality violations. These findings indicate that control-aware, power-balance-constrained probabilistic PUE forecasts can provide a reliable basis for coordinated data center DSM and microgrid dispatch. Full article

Renewable-powered agricultural water systems are often assessed as solar-pumping devices, but their sustainability depends on a service chain linking crop-water demand, hydraulic duty point, power electronics, storage, water quality, governance, operation and end-of-life management. This structured integrative review synthesizes peer-reviewed and practice-oriented evidence on photovoltaic pumping, hybrid renewable irrigation, grid-interactive pumps, micro-hydro assistance and renewable-powered brackish-water reverse osmosis (PV-RO). Evidence was screened across four source families and coded by service function, energy architecture, hydraulic duty and dominant sustainability pathway; recurring combinations were consolidated using explicit separation and merge rules. It develops an archetype-based screening framework for ex ante appraisal of irrigation, desalination and circularity risks. Seven technology-service archetypes are identified: direct PV pumping, PV-to-tank pumping, PV with electrical buffering, grid-interactive PV pumping, PV–wind hybrid irrigation, micro-hydro-assisted irrigation and PV-RO water making. The framework links each archetype to its operating envelope, evidence maturity, enabling subsystems, sustainability pathways, minimum indicators and ordinal triggers for deeper due diligence. Hydraulic storage is usually the lowest-regret reliability buffer for open-field irrigation, whereas batteries are justified mainly when pressure stability, fertigation timing or night-time operation has high agronomic value. PV-RO is a distinct water-making archetype and is environmentally defensible only where feed-water characterization, energy recovery, pretreatment, product-water agronomy, membrane management and permitted concentrate disposal are embedded in design. Two synthetic applications demonstrate archetype selection and due-diligence escalation. Responsible deployment requires service-oriented screening that integrates hydraulic design, groundwater governance, procurement quality assurance, circularity obligations and social inclusion before field implementation. Full article

The building sector is one of the largest contributors to global energy demand and carbon emissions, making the transition to net-zero buildings (NZBs) a critical component of climate change mitigation strategies. However, the persistent building energy performance gap (BEPG), defined as the discrepancy between predicted and actual energy consumption, continues to hinder the achievement of net-zero operational performance. Accordingly, this review examines the role of artificial intelligence (AI) in enabling NZBs through the integration of energy-efficient building systems, renewable energy technologies, and intelligent operational control. A comprehensive review of the literature published between 2018 and 2025 was conducted, focusing on three complementary domains: heating, ventilation, and air conditioning (HVAC) system efficiency as the demand-side pillar, renewable energy integration as the supply-side pillar, and AI as the enabling layer connecting both domains. Synthesis of the reviewed literature reveals that demand-side HVAC technologies achieve energy savings ranging from 20% to 67%, while supply-side renewable energy integration increases photovoltaic (PV) self-consumption by 11–13%. Furthermore, AI-driven optimization, particularly through reinforcement learning (22.3% ± 8.4% energy savings) and digital twins (up to 70% renewable energy utilization), substantially enhances building performance within integrated energy management frameworks. The reviewed studies further demonstrate that AI techniques, including machine learning, deep learning, reinforcement learning, and digital twins, enable accurate energy forecasting (R² > 0.90), intelligent operational control, and effective coordination of integrated PV–battery energy storage system–electric vehicle systems, improving building energy flexibility and reducing grid fluctuations by up to 12.78%. Despite these advances, challenges related to data quality, interoperability, model explainability, cybersecurity, and limited large-scale real-world validation remain significant barriers to widespread adoption. Overall, the evidence indicates that AI serves as a key enabler for reducing the BEPG and improving the reliability, resilience, and operational efficiency of NZBs, thereby supporting the transition toward intelligent, low-carbon built environments. Full article

The rise in the need for sustainable energy has facilitated the advancement of perovskite solar cells (PSCs) as potential substitutes for traditional photovoltaic technologies. Nevertheless, their performance is very sensitive to environmental factors, especially temperature, which influences the charge transport and recombination processes. This paper examines the thermal effect on the electrical characteristics and impedance response of lead-free PSCs in accordance with the FTO/ETL (C₆₀, PCBM, SnS₂, ZnSe)/CH₃NH₃SnBr₃/Cu₂O configuration. The experiments were performed with SCAPS-1D under usual illumination, using a combination of current-voltage analysis and impedance spectroscopy between 270 and 400 K. The findings indicate that there is a significant reduction in open-circuit voltage with higher temperature, whereas the short-circuit current density does not change much. The enhancement of the fill factor increases and then decreases with increased temperature, leading to a net decrease in power conversion efficiency because of the increased recombination. The impedance analysis is also an indicator of lower recombination resistance and accelerated charge carrier dynamics. These results demonstrate that thermal control and interface optimization can be important for enhancing PSC performance. Full article

Microgrids are emerging as a promising solution to enhance renewable energy integration, energy reliability, electricity access, and sustainability in Morocco. This paper reviews the development of sustainable microgrids in the Moroccan context by analyzing existing projects, system configurations, control approaches, and energy management strategies. In line with Morocco’s objective of reaching 52% renewable electricity capacity by 2030, the reviewed studies show that hybrid microgrids combining photovoltaic, wind, battery storage, diesel backup, and pumped hydro storage can improve energy autonomy, reduce dependence on fossil fuels, and support a more sustainable energy transition. Across the reviewed case studies, reported performance indicators include renewable energy penetration of up to 97%, a Loss of Power Supply Probability (LPSP) of 0.0489, Levelized Cost of Energy (LCOE) values ranging from 0.038 to 0.17 USD/kWh, and energy cost reductions of up to 20.7% in building-integrated photovoltaic applications. These values are study-specific and should be interpreted as indicative performance outcomes rather than directly comparable benchmarks, since they depend on system size, load profile, storage technology, tariff structure, and optimization assumptions. The review also highlights the role of advanced control and optimization techniques, such as particle swarm optimization, model predictive control, equilibrium optimizer, and adaptive energy management systems, in improving power balance, reliability, cost-effectiveness, and sustainability. Finally, this paper identifies the main technical, economic, regulatory, and institutional barriers limiting large-scale sustainable microgrid deployment in Morocco and proposes recommendations to support decentralized, resilient, and environmentally sustainable renewable energy systems. Full article

Municipal solid waste (MSW) incineration with energy recovery is embedded in national decarbonization strategies but emits fossil CO₂ from plastic-derived combustion, challenging the long-term sustainability of waste-to-energy systems. Carbon capture and utilization (CCU) offers a potential mitigation route, yet assessments rarely link technology economics, environmental performance, and the carbon-pricing instruments that would finance deployment. This study develops a break-even carbon-pricing framework integrating life-cycle CO₂ emissions (LCCO₂) and discounted annualized life-cycle cost (LCC; capital-recovery-factor annualization at a 4% real discount rate) for two CCU routes—methanation and methanol synthesis—applied to a 300 t/day Japanese incineration facility (84,000 t/y) under 2024 and 2050 energy-system conditions, thereby quantifying the environmental and the economic dimensions of sustainable CCU deployment in the waste sector. Two complementary indicators are distinguished: an incremental break-even carbon price, the price at which adding CCU to the existing waste-to-energy facility becomes economically neutral, and a plant-level cash balance price. Under the product-system boundary and photovoltaic-electrolysis hydrogen, both routes show lower life-cycle emissions than the baseline in both years; the magnitude—and, for methanation in 2024, the sign—of the net climate benefit depends on the downstream-use accounting boundary. The incremental break-even price for methanol falls from 20.3 × 10⁴ JPY/t-CO₂ (≈1293 USD/t-CO₂) in 2024 to 1.90 × 10⁴ JPY/t-CO₂ (≈122 USD/t-CO₂) in 2050, while that for methanation falls from 32.2 × 10⁴ JPY/t-CO₂ to 0.75 × 10⁴ JPY/t-CO₂ (≈48 USD/t-CO₂)—about half the 2023 EU ETS average price—and approaches zero at approximately a one-third capital subsidy. This collapse is driven largely by the assumed hydrogen-price decline (100 → 20 JPY/Nm³); hydrogen-supply policy, rather than carbon pricing alone, therefore appears to be the dominant lever for making CCU at MSW incineration a viable contribution to sustainable, carbon-neutral waste management. Sensitivity analyses covering the discount rate (2–8%), plant scale (300–900 t/day), methane leakage, product-market absorption, and hydrogen delivered price premiums support the robustness of this sequencing conclusion. Full article

Accurate prediction of photovoltaic (PV) cell power from electroluminescence (EL) images is a key enabler for automated quality assessment and performance estimation in PV manufacturing and diagnostics. However, most existing image-based deep learning models are developed and evaluated for a single PV cell technology, limiting their ability to generalize across the wide variety of cell types used in practice. This work investigates the impact of PV cell technology on power prediction accuracy and proposes a novel Convolutional Hierarchical Mixture of Experts (CHME) architecture to overcome these generalization limitations. First, convolutional neural networks and feature-based machine learning models are evaluated on multiple PV cell technologies. While technology-specific convolutional models achieve low mean absolute errors (MAEs) of 0.02–0.04 when tested on the same cell type, their performance deteriorates substantially (MAEs of 0.08–0.23) when applied to different technologies. Feature-based models exhibit greater robustness across technologies but at the cost of lower prediction accuracy. To address these limitations, the proposed CHME model combines multiple pretrained technology-specific convolutional experts with a discriminator network that automatically identifies the PV cell technology and selects the most appropriate expert for power prediction. Experimental results demonstrate that CHME achieves the best overall performance, reducing the MAE to 0.0262 compared with 0.0298 for the best standalone convolutional model, while preserving adaptability to heterogeneous datasets. These results demonstrate that explicitly accounting for PV cell technology significantly improves image-based power prediction and that the proposed hierarchical mixture-of-experts framework provides an accurate, scalable, and easily retrainable solution for real-world PV diagnostic systems. Full article

Perovskite tandem solar cells (TSCs) have emerged as a transformative photovoltaic technology, offering a viable pathway to surpass the Shockley-Queisser limit of single-junction devices by enabling broader solar spectrum utilization and reduced thermalization losses. This review provides a comprehensive overview of recent progress in perovskite-based TSCs, covering four major device architectures: perovskite/silicon, perovskite/CIGS, all-perovskite, and perovskite/organic TSCs. We systematically discuss the fundamental working principles, including bandgap engineering, charge generation and separation, and current-voltage matching, followed by an in-depth analysis of strategies for perovskite layer regulation, interface engineering, and transport-layer optimization. Key advancements, such as compositional engineering, defect passivation, crystallization control, and optical management, have synergistically pushed power conversion efficiencies (PCEs) beyond 34% for perovskite/silicon TSCs and over 28% for all-perovskite and perovskite/organic configurations. Despite these achievements, critical challenges remain, including material instability, halide phase segregation, lead toxicity, scalable fabrication, and cost-effective integration. This review also outlines future perspectives, emphasizing the development of lead-free perovskites, novel charge-transport materials, advanced encapsulation techniques, and large-area manufacturing processes. With continued interdisciplinary efforts, perovskite TSCs hold great promise for driving the global transition toward sustainable and low-carbon energy systems. Full article

Underwater wireless optical communication (UWOC) has emerged as a key technology for high-speed, low-latency data transmission in aquatic environments, enabling applications in autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), subsea sensor networks, and the Internet of Underwater Things (IoUT). This paper reviews photodetector technologies that shape UWOC system performance, covering both mature and emerging detector classes. We discuss the operating principles, key parameters, and practical trade-offs of photomultiplier tubes (PMTs), p-i-n photodiodes (PINs), avalanche photodiodes (APDs), single-photon avalanche diodes (SPADs), and silicon photomultipliers (SiPMs/MPPCs). We also present emerging photodetector technologies, including perovskite-based structures, SiC photoelectrochemical devices, scintillating optical fibers, and photovoltaic solar cells. A comparative analysis of reported UWOC experiments reveals a clear sensitivity–bandwidth trade-off among detector technologies: PIN-based receivers achieve the highest data rates (up to 25 Gbps) but are generally restricted to short-range links, whereas SPAD- and SiPM-based receivers provide sensitivities below −80 dBm and support transmission distances exceeding 200 m, at the cost of moderate data rates. The findings indicate that SiPM/MPPC arrays currently offer the most promising compromise between sensitivity and data rate for long-range UWOC applications. Full article

The lack of freshwater in the world requires a paradigm shift from linear water consumption to resilient and low-energy desalination technologies. Although reverse osmosis (RO) is the standard in the industry, its usefulness is essentially constrained by concentration polarization (CP) and non-useful hydraulic pressure losses. This paper applies a high-fidelity computational model in ANSYS Fluent 2022 R1 to conduct a comparative parametric evaluation of hexagonal and sinusoidal feed-spacer geometries relative to a baseline grid configuration. The solute concentration gradients at the fluid–membrane interface were solved using a 3D species transport model, which was optimized using one-micron near-wall inflation layers. The hexagonal configuration produced the lowest maximum membrane-surface salt mass fraction, decreasing it from 0.1127 kg/kg for the baseline grid to 0.0429 kg/kg, corresponding to a 61.9% reduction. Although the hexagonal design required an inlet pressure of 205.7 Pa, it produced a more favorable normalized mass-transfer–friction trade-off than the sinusoidal configuration (447.8 Pa), with a System Performance Index (η) of 2.53. These results demonstrate comparative micro-scale improvements in concentration polarization control and hydraulic performance under the simulated conditions. Experimental testing and system-level modeling are required before conclusions can be drawn regarding full-module energy consumption, photovoltaic integration, long-term fouling behavior, or economic feasibility. This study is consistent with the emerging Concepts and design for sustainability, whereby a circular and energy-efficient water economy is facilitated through an innovative mechanical design. Full article

The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations. Full article

Photovoltaic (PV) technology is now one of the main options for expanding the power of low-carbon electricity generation. However, in practical operation, PV systems still face several persistent difficulties, including the variability of solar irradiance, gradual performance degradation, fault occurrence, suboptimal control, and the growing complexity of grid-connected operation. These issues explain why artificial intelligence (AI) has become increasingly relevant in PV research, not only as a prediction tool, but also to improve monitoring, control, diagnosis, and decision-making. This review investigates the applications of AI in the major stages of the PV system lifecycle: solar resource assessment, power forecasting, fault detection, condition monitoring, system sizing, maximum power point tracking (MPPT), and grid integration. Rather than treating these applications as separate research topics, the review attempts to connect them through the common factors that determine their practical value: data quality, sensing configuration, model complexity, physical operating conditions, and deployment constraints. The reviewed studies indicate that AI-based MPPT methods can achieve tracking efficiencies close to 99%, while recent forecasting models, particularly LSTM, CNN–LSTM, and transformer-based architectures, can reduce prediction errors under changing weather conditions. At the same time, PV fault detection is moving beyond electroluminescence image classification toward more practical multimodal strategies that combine infrared thermography, RGB and drone imagery, electrical measurements, and SCADA/IoT data. Nevertheless, the progress reported in the literature should be interpreted with caution. Many proposed models are still evaluated on limited or non-standardized datasets, and their performance may decrease when they are transferred to different PV technologies, climates, fault severities, or operating conditions. Other recurring limitations include class imbalance, high computational cost, weak generalization, and the limited interpretability of deep-learning models. For this reason, hybrid neural networks, explainable AI, physics-informed learning, edge-AI, federated learning, and quantum machine learning are discussed as possible directions for making AI-based PV solutions more reliable and deployable. This review aims to critically synthesize recent advances and remaining gaps in order to support the practical integration of AI into efficient, reliable, and sustainable PV systems. Full article